Overview

Dataset statistics

Number of variables25
Number of observations79293
Missing cells220864
Missing cells (%)11.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.1 MiB
Average record size in memory200.0 B

Variable types

Numeric14
Categorical9
Boolean2

Warnings

event_date has a high cardinality: 12638 distinct values High cardinality
location has a high cardinality: 25264 distinct values High cardinality
injury_severity has a high cardinality: 124 distinct values High cardinality
make has a high cardinality: 6707 distinct values High cardinality
model has a high cardinality: 11330 distinct values High cardinality
Unnamed: 0 is highly correlated with Year and 1 other fieldsHigh correlation
total_fatal_injuries is highly correlated with injuriesHigh correlation
total_serious_injuries is highly correlated with injuriesHigh correlation
total_minor_injuries is highly correlated with injuriesHigh correlation
total_uninjured is highly correlated with pax_onboard and 1 other fieldsHigh correlation
Year is highly correlated with Unnamed: 0 and 1 other fieldsHigh correlation
injuries is highly correlated with total_fatal_injuries and 2 other fieldsHigh correlation
pax_onboard is highly correlated with total_uninjured and 1 other fieldsHigh correlation
survived is highly correlated with total_uninjured and 1 other fieldsHigh correlation
df_index is highly correlated with Unnamed: 0 and 1 other fieldsHigh correlation
Unnamed: 0 is highly correlated with Year and 1 other fieldsHigh correlation
total_fatal_injuries is highly correlated with injuries and 2 other fieldsHigh correlation
total_minor_injuries is highly correlated with injuriesHigh correlation
total_uninjured is highly correlated with injuries and 2 other fieldsHigh correlation
Year is highly correlated with Unnamed: 0 and 1 other fieldsHigh correlation
injuries is highly correlated with total_fatal_injuries and 3 other fieldsHigh correlation
pax_onboard is highly correlated with survivedHigh correlation
fatality_percentage is highly correlated with total_fatal_injuries and 3 other fieldsHigh correlation
survived is highly correlated with total_fatal_injuries and 3 other fieldsHigh correlation
df_index is highly correlated with Unnamed: 0 and 1 other fieldsHigh correlation
Unnamed: 0 is highly correlated with Year and 1 other fieldsHigh correlation
total_fatal_injuries is highly correlated with injuries and 2 other fieldsHigh correlation
total_uninjured is highly correlated with injuries and 1 other fieldsHigh correlation
Year is highly correlated with Unnamed: 0 and 1 other fieldsHigh correlation
injuries is highly correlated with total_fatal_injuries and 2 other fieldsHigh correlation
pax_onboard is highly correlated with survivedHigh correlation
fatality_percentage is highly correlated with total_fatal_injuries and 2 other fieldsHigh correlation
survived is highly correlated with total_fatal_injuries and 3 other fieldsHigh correlation
df_index is highly correlated with Unnamed: 0 and 1 other fieldsHigh correlation
Unnamed: 0 is highly correlated with df_index and 1 other fieldsHigh correlation
amateur_build is highly correlated with AmateurBuiltHigh correlation
aircraft_damage is highly correlated with weather_conditions and 2 other fieldsHigh correlation
total_serious_injuries is highly correlated with injuries and 2 other fieldsHigh correlation
pax_onboard is highly correlated with engine_type and 2 other fieldsHigh correlation
df_index is highly correlated with Unnamed: 0 and 1 other fieldsHigh correlation
number_of_engines is highly correlated with engine_typeHigh correlation
injuries is highly correlated with total_serious_injuries and 2 other fieldsHigh correlation
total_fatal_injuries is highly correlated with total_serious_injuries and 1 other fieldsHigh correlation
engine_type is highly correlated with pax_onboard and 2 other fieldsHigh correlation
weather_conditions is highly correlated with aircraft_damageHigh correlation
Year is highly correlated with Unnamed: 0 and 1 other fieldsHigh correlation
fatality_percentage is highly correlated with aircraft_damageHigh correlation
total_minor_injuries is highly correlated with total_serious_injuries and 1 other fieldsHigh correlation
survived is highly correlated with pax_onboard and 2 other fieldsHigh correlation
AmateurBuilt is highly correlated with amateur_buildHigh correlation
phase_of_flight is highly correlated with aircraft_damageHigh correlation
total_uninjured is highly correlated with pax_onboard and 1 other fieldsHigh correlation
amateur_build is highly correlated with AmateurBuiltHigh correlation
AmateurBuilt is highly correlated with amateur_buildHigh correlation
aircraft_damage has 2410 (3.0%) missing values Missing
number_of_engines has 3986 (5.0%) missing values Missing
engine_type has 3374 (4.3%) missing values Missing
total_fatal_injuries has 23309 (29.4%) missing values Missing
total_serious_injuries has 25551 (32.2%) missing values Missing
total_minor_injuries has 24460 (30.8%) missing values Missing
total_uninjured has 12344 (15.6%) missing values Missing
phase_of_flight has 6054 (7.6%) missing values Missing
injuries has 29557 (37.3%) missing values Missing
pax_onboard has 29671 (37.4%) missing values Missing
fatality_percentage has 29698 (37.5%) missing values Missing
survived has 29671 (37.4%) missing values Missing
total_fatal_injuries is highly skewed (γ1 = 29.51903889) Skewed
total_serious_injuries is highly skewed (γ1 = 37.45361961) Skewed
total_minor_injuries is highly skewed (γ1 = 66.84890696) Skewed
injuries is highly skewed (γ1 = 30.77744311) Skewed
Unnamed: 0 is uniformly distributed Uniform
df_index is uniformly distributed Uniform
Unnamed: 0 has unique values Unique
df_index has unique values Unique
number_of_engines has 1275 (1.6%) zeros Zeros
total_fatal_injuries has 40092 (50.6%) zeros Zeros
total_serious_injuries has 42660 (53.8%) zeros Zeros
total_minor_injuries has 40064 (50.5%) zeros Zeros
total_uninjured has 19126 (24.1%) zeros Zeros
injuries has 26956 (34.0%) zeros Zeros
fatality_percentage has 40061 (50.5%) zeros Zeros
survived has 7829 (9.9%) zeros Zeros

Reproduction

Analysis started2021-09-06 20:05:28.358153
Analysis finished2021-09-06 20:05:54.773251
Duration26.42 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct79293
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39646
Minimum0
Maximum79292
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size619.6 KiB
2021-09-06T13:05:54.823018image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3964.6
Q119823
median39646
Q359469
95-th percentile75327.4
Maximum79292
Range79292
Interquartile range (IQR)39646

Descriptive statistics

Standard deviation22890.06178
Coefficient of variation (CV)0.5773611912
Kurtosis-1.2
Mean39646
Median Absolute Deviation (MAD)19823
Skewness-2.103152104 × 10-17
Sum3143650278
Variance523954928.5
MonotonicityNot monotonic
2021-09-06T13:05:54.902809image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20471
 
< 0.1%
47591
 
< 0.1%
108961
 
< 0.1%
88491
 
< 0.1%
149941
 
< 0.1%
129471
 
< 0.1%
27081
 
< 0.1%
6611
 
< 0.1%
68061
 
< 0.1%
272881
 
< 0.1%
Other values (79283)79283
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
792921
< 0.1%
792911
< 0.1%
792901
< 0.1%
792891
< 0.1%
792881
< 0.1%
792871
< 0.1%
792861
< 0.1%
792851
< 0.1%
792841
< 0.1%
792831
< 0.1%

event_date
Categorical

HIGH CARDINALITY

Distinct12638
Distinct (%)15.9%
Missing0
Missing (%)0.0%
Memory size619.6 KiB
1982-05-16
 
25
1984-06-30
 
25
2000-07-08
 
25
1983-06-05
 
24
1983-08-05
 
24
Other values (12633)
79170 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters792930
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique542 ?
Unique (%)0.7%

Sample

1st row1982-06-13
2nd row1982-07-01
3rd row1982-07-16
4th row1982-08-21
5th row1982-08-24

Common Values

ValueCountFrequency (%)
1982-05-1625
 
< 0.1%
1984-06-3025
 
< 0.1%
2000-07-0825
 
< 0.1%
1983-06-0524
 
< 0.1%
1983-08-0524
 
< 0.1%
1986-05-1724
 
< 0.1%
1984-08-2524
 
< 0.1%
1983-05-2823
 
< 0.1%
1988-08-0723
 
< 0.1%
1982-10-0323
 
< 0.1%
Other values (12628)79053
99.7%

Length

2021-09-06T13:05:55.059448image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1982-05-1625
 
< 0.1%
1984-06-3025
 
< 0.1%
2000-07-0825
 
< 0.1%
1983-06-0524
 
< 0.1%
1983-08-0524
 
< 0.1%
1986-05-1724
 
< 0.1%
1984-08-2524
 
< 0.1%
1983-05-2823
 
< 0.1%
1988-08-0723
 
< 0.1%
1982-10-0323
 
< 0.1%
Other values (12628)79053
99.7%

Most occurring characters

ValueCountFrequency (%)
0159256
20.1%
-158586
20.0%
1126056
15.9%
992245
11.6%
284143
10.6%
848463
 
6.1%
327198
 
3.4%
624806
 
3.1%
724363
 
3.1%
524355
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number634344
80.0%
Dash Punctuation158586
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0159256
25.1%
1126056
19.9%
992245
14.5%
284143
13.3%
848463
 
7.6%
327198
 
4.3%
624806
 
3.9%
724363
 
3.8%
524355
 
3.8%
423459
 
3.7%
Dash Punctuation
ValueCountFrequency (%)
-158586
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common792930
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0159256
20.1%
-158586
20.0%
1126056
15.9%
992245
11.6%
284143
10.6%
848463
 
6.1%
327198
 
3.4%
624806
 
3.1%
724363
 
3.1%
524355
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII792930
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0159256
20.1%
-158586
20.0%
1126056
15.9%
992245
11.6%
284143
10.6%
848463
 
6.1%
327198
 
3.4%
624806
 
3.1%
724363
 
3.1%
524355
 
3.1%

location
Categorical

HIGH CARDINALITY

Distinct25264
Distinct (%)31.9%
Missing0
Missing (%)0.0%
Memory size619.6 KiB
ANCHORAGE, AK
 
372
MIAMI, FL
 
185
CHICAGO, IL
 
169
ALBUQUERQUE, NM
 
165
HOUSTON, TX
 
155
Other values (25259)
78247 

Length

Max length61
Median length12
Mean length12.93819127
Min length4

Characters and Unicode

Total characters1025908
Distinct characters82
Distinct categories12 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14896 ?
Unique (%)18.8%

Sample

1st rowCAMBRIA, NY
2nd rowMCWHORTER, KY
3rd rowFREDERICK, MD
4th rowVENTURA, CA
5th rowSIDNEY, NE

Common Values

ValueCountFrequency (%)
ANCHORAGE, AK372
 
0.5%
MIAMI, FL185
 
0.2%
CHICAGO, IL169
 
0.2%
ALBUQUERQUE, NM165
 
0.2%
HOUSTON, TX155
 
0.2%
Anchorage, AK140
 
0.2%
FAIRBANKS, AK138
 
0.2%
ORLANDO, FL114
 
0.1%
TUCSON, AZ107
 
0.1%
ENGLEWOOD, CO107
 
0.1%
Other values (25254)77641
97.9%

Length

2021-09-06T13:05:55.216778image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ca8179
 
4.5%
tx5265
 
2.9%
fl5228
 
2.9%
ak5171
 
2.9%
az2554
 
1.4%
co2508
 
1.4%
wa2394
 
1.3%
il1914
 
1.1%
mi1896
 
1.0%
city1883
 
1.0%
Other values (12575)143931
79.6%

Most occurring characters

ValueCountFrequency (%)
101630
 
9.9%
,79108
 
7.7%
A74922
 
7.3%
N47000
 
4.6%
L45800
 
4.5%
E44628
 
4.4%
O41931
 
4.1%
I36346
 
3.5%
T34118
 
3.3%
R33380
 
3.3%
Other values (72)487045
47.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter609663
59.4%
Lowercase Letter233302
 
22.7%
Space Separator101630
 
9.9%
Other Punctuation80593
 
7.9%
Decimal Number464
 
< 0.1%
Dash Punctuation216
 
< 0.1%
Open Punctuation12
 
< 0.1%
Close Punctuation12
 
< 0.1%
Format9
 
< 0.1%
Control5
 
< 0.1%
Other values (2)2
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A74922
 
12.3%
N47000
 
7.7%
L45800
 
7.5%
E44628
 
7.3%
O41931
 
6.9%
I36346
 
6.0%
T34118
 
5.6%
R33380
 
5.5%
C33249
 
5.5%
S30350
 
5.0%
Other values (18)187939
30.8%
Lowercase Letter
ValueCountFrequency (%)
a29434
12.6%
e25946
11.1%
n21525
9.2%
o19646
 
8.4%
l17958
 
7.7%
i17473
 
7.5%
r17236
 
7.4%
t13286
 
5.7%
s11330
 
4.9%
d7736
 
3.3%
Other values (16)51732
22.2%
Decimal Number
ValueCountFrequency (%)
183
17.9%
061
13.1%
260
12.9%
553
11.4%
345
9.7%
437
8.0%
734
7.3%
633
 
7.1%
831
 
6.7%
927
 
5.8%
Other Punctuation
ValueCountFrequency (%)
,79108
98.2%
.1211
 
1.5%
'166
 
0.2%
?73
 
0.1%
/29
 
< 0.1%
#3
 
< 0.1%
§2
 
< 0.1%
&1
 
< 0.1%
Control
ValueCountFrequency (%)
œ2
40.0%
2
40.0%
1
20.0%
Space Separator
ValueCountFrequency (%)
101630
100.0%
Dash Punctuation
ValueCountFrequency (%)
-216
100.0%
Open Punctuation
ValueCountFrequency (%)
(12
100.0%
Close Punctuation
ValueCountFrequency (%)
)12
100.0%
Format
ValueCountFrequency (%)
­9
100.0%
Math Symbol
ValueCountFrequency (%)
±1
100.0%
Modifier Symbol
ValueCountFrequency (%)
`1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin842965
82.2%
Common182943
 
17.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
A74922
 
8.9%
N47000
 
5.6%
L45800
 
5.4%
E44628
 
5.3%
O41931
 
5.0%
I36346
 
4.3%
T34118
 
4.0%
R33380
 
4.0%
C33249
 
3.9%
S30350
 
3.6%
Other values (44)421241
50.0%
Common
ValueCountFrequency (%)
101630
55.6%
,79108
43.2%
.1211
 
0.7%
-216
 
0.1%
'166
 
0.1%
183
 
< 0.1%
?73
 
< 0.1%
061
 
< 0.1%
260
 
< 0.1%
553
 
< 0.1%
Other values (18)282
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1025878
> 99.9%
Latin 1 Sup30
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
101630
 
9.9%
,79108
 
7.7%
A74922
 
7.3%
N47000
 
4.6%
L45800
 
4.5%
E44628
 
4.4%
O41931
 
4.1%
I36346
 
3.5%
T34118
 
3.3%
R33380
 
3.3%
Other values (65)487015
47.5%
Latin 1 Sup
ValueCountFrequency (%)
Â14
46.7%
­9
30.0%
§2
 
6.7%
œ2
 
6.7%
Ã1
 
3.3%
±1
 
3.3%
1
 
3.3%

injury_severity
Categorical

HIGH CARDINALITY

Distinct124
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size619.6 KiB
Non-Fatal
60025 
Fatal(1)
7830 
Fatal(2)
 
4618
Incident
 
3175
Fatal(3)
 
1450
Other values (119)
 
2195

Length

Max length11
Median length9
Mean length8.769513072
Min length8

Characters and Unicode

Total characters695361
Distinct characters28
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique67 ?
Unique (%)0.1%

Sample

1st rowNon-Fatal
2nd rowNon-Fatal
3rd rowFatal(1)
4th rowNon-Fatal
5th rowNon-Fatal

Common Values

ValueCountFrequency (%)
Non-Fatal60025
75.7%
Fatal(1)7830
 
9.9%
Fatal(2)4618
 
5.8%
Incident3175
 
4.0%
Fatal(3)1450
 
1.8%
Fatal(4)1012
 
1.3%
Fatal(5)311
 
0.4%
Unavailable220
 
0.3%
Fatal(6)196
 
0.2%
Fatal(7)83
 
0.1%
Other values (114)373
 
0.5%

Length

2021-09-06T13:05:55.370165image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
non-fatal60025
75.7%
fatal(17830
 
9.9%
fatal(24618
 
5.8%
incident3175
 
4.0%
fatal(31450
 
1.8%
fatal(41012
 
1.3%
fatal(5311
 
0.4%
unavailable220
 
0.3%
fatal(6196
 
0.2%
fatal(783
 
0.1%
Other values (114)373
 
0.5%

Most occurring characters

ValueCountFrequency (%)
a152456
21.9%
t79073
11.4%
l76338
11.0%
F75898
10.9%
n66595
9.6%
N60025
 
8.6%
o60025
 
8.6%
-60025
 
8.6%
(15873
 
2.3%
)15873
 
2.3%
Other values (18)33180
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter448067
64.4%
Uppercase Letter139318
 
20.0%
Dash Punctuation60025
 
8.6%
Decimal Number16205
 
2.3%
Open Punctuation15873
 
2.3%
Close Punctuation15873
 
2.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a152456
34.0%
t79073
17.6%
l76338
17.0%
n66595
14.9%
o60025
 
13.4%
i3395
 
0.8%
e3395
 
0.8%
c3175
 
0.7%
d3175
 
0.7%
v220
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
18034
49.6%
24692
29.0%
31498
 
9.2%
41064
 
6.6%
5354
 
2.2%
6222
 
1.4%
7115
 
0.7%
894
 
0.6%
071
 
0.4%
961
 
0.4%
Uppercase Letter
ValueCountFrequency (%)
F75898
54.5%
N60025
43.1%
I3175
 
2.3%
U220
 
0.2%
Dash Punctuation
ValueCountFrequency (%)
-60025
100.0%
Open Punctuation
ValueCountFrequency (%)
(15873
100.0%
Close Punctuation
ValueCountFrequency (%)
)15873
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin587385
84.5%
Common107976
 
15.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a152456
26.0%
t79073
13.5%
l76338
13.0%
F75898
12.9%
n66595
11.3%
N60025
 
10.2%
o60025
 
10.2%
i3395
 
0.6%
e3395
 
0.6%
I3175
 
0.5%
Other values (5)7010
 
1.2%
Common
ValueCountFrequency (%)
-60025
55.6%
(15873
 
14.7%
)15873
 
14.7%
18034
 
7.4%
24692
 
4.3%
31498
 
1.4%
41064
 
1.0%
5354
 
0.3%
6222
 
0.2%
7115
 
0.1%
Other values (3)226
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII695361
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a152456
21.9%
t79073
11.4%
l76338
11.0%
F75898
10.9%
n66595
9.6%
N60025
 
8.6%
o60025
 
8.6%
-60025
 
8.6%
(15873
 
2.3%
)15873
 
2.3%
Other values (18)33180
 
4.8%

aircraft_damage
Categorical

HIGH CORRELATION
MISSING

Distinct3
Distinct (%)< 0.1%
Missing2410
Missing (%)3.0%
Memory size619.6 KiB
Substantial
57049 
Destroyed
17322 
Minor
 
2512

Length

Max length11
Median length11
Mean length10.3533551
Min length5

Characters and Unicode

Total characters795997
Distinct characters16
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDestroyed
2nd rowDestroyed
3rd rowDestroyed
4th rowDestroyed
5th rowSubstantial

Common Values

ValueCountFrequency (%)
Substantial57049
71.9%
Destroyed17322
 
21.8%
Minor2512
 
3.2%
(Missing)2410
 
3.0%

Length

2021-09-06T13:05:55.648988image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-06T13:05:55.691582image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
substantial57049
74.2%
destroyed17322
 
22.5%
minor2512
 
3.3%

Most occurring characters

ValueCountFrequency (%)
t131420
16.5%
a114098
14.3%
s74371
9.3%
n59561
7.5%
i59561
7.5%
S57049
7.2%
u57049
7.2%
b57049
7.2%
l57049
7.2%
e34644
 
4.4%
Other values (6)94146
11.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter719114
90.3%
Uppercase Letter76883
 
9.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t131420
18.3%
a114098
15.9%
s74371
10.3%
n59561
8.3%
i59561
8.3%
u57049
7.9%
b57049
7.9%
l57049
7.9%
e34644
 
4.8%
r19834
 
2.8%
Other values (3)54478
7.6%
Uppercase Letter
ValueCountFrequency (%)
S57049
74.2%
D17322
 
22.5%
M2512
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
Latin795997
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t131420
16.5%
a114098
14.3%
s74371
9.3%
n59561
7.5%
i59561
7.5%
S57049
7.2%
u57049
7.2%
b57049
7.2%
l57049
7.2%
e34644
 
4.4%
Other values (6)94146
11.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII795997
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t131420
16.5%
a114098
14.3%
s74371
9.3%
n59561
7.5%
i59561
7.5%
S57049
7.2%
u57049
7.2%
b57049
7.2%
l57049
7.2%
e34644
 
4.4%
Other values (6)94146
11.8%

make
Categorical

HIGH CARDINALITY

Distinct6707
Distinct (%)8.5%
Missing89
Missing (%)0.1%
Memory size619.6 KiB
CESSNA
24847 
PIPER
13529 
BEECH
4881 
BELL
 
2467
BOEING
 
2153
Other values (6702)
31327 

Length

Max length33
Median length6
Mean length7.494432099
Min length2

Characters and Unicode

Total characters593589
Distinct characters47
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5425 ?
Unique (%)6.8%

Sample

1st row107.5 FLYING CORPORATION
2nd row1200
3rd row177MF LLC
4th row1977 COLFER-CHAN
5th row1ST FTR GP

Common Values

ValueCountFrequency (%)
CESSNA24847
31.3%
PIPER13529
17.1%
BEECH4881
 
6.2%
BELL2467
 
3.1%
BOEING2153
 
2.7%
MOONEY1209
 
1.5%
GRUMMAN1126
 
1.4%
ROBINSON1005
 
1.3%
BELLANCA987
 
1.2%
HUGHES881
 
1.1%
Other values (6697)26119
32.9%

Length

2021-09-06T13:05:55.831706image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cessna24888
26.2%
piper13569
 
14.3%
beech4889
 
5.2%
bell2514
 
2.7%
boeing2231
 
2.4%
grumman1456
 
1.5%
robinson1305
 
1.4%
mooney1249
 
1.3%
bellanca989
 
1.0%
american956
 
1.0%
Other values (5893)40779
43.0%

Most occurring characters

ValueCountFrequency (%)
E82988
14.0%
S66877
11.3%
A56791
9.6%
N50502
 
8.5%
C47336
 
8.0%
R44285
 
7.5%
I35920
 
6.1%
P32692
 
5.5%
O26516
 
4.5%
L22719
 
3.8%
Other values (37)126963
21.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter573692
96.6%
Space Separator15621
 
2.6%
Other Punctuation2496
 
0.4%
Dash Punctuation989
 
0.2%
Open Punctuation338
 
0.1%
Close Punctuation336
 
0.1%
Decimal Number112
 
< 0.1%
Math Symbol5
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E82988
14.5%
S66877
11.7%
A56791
9.9%
N50502
8.8%
C47336
8.3%
R44285
7.7%
I35920
 
6.3%
P32692
 
5.7%
O26516
 
4.6%
L22719
 
4.0%
Other values (16)107066
18.7%
Decimal Number
ValueCountFrequency (%)
120
17.9%
017
15.2%
717
15.2%
213
11.6%
510
8.9%
39
8.0%
69
8.0%
87
 
6.2%
46
 
5.4%
94
 
3.6%
Other Punctuation
ValueCountFrequency (%)
.1564
62.7%
,321
 
12.9%
/283
 
11.3%
&271
 
10.9%
'35
 
1.4%
?22
 
0.9%
Space Separator
ValueCountFrequency (%)
15621
100.0%
Dash Punctuation
ValueCountFrequency (%)
-989
100.0%
Open Punctuation
ValueCountFrequency (%)
(338
100.0%
Close Punctuation
ValueCountFrequency (%)
)336
100.0%
Math Symbol
ValueCountFrequency (%)
+5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin573692
96.6%
Common19897
 
3.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
E82988
14.5%
S66877
11.7%
A56791
9.9%
N50502
8.8%
C47336
8.3%
R44285
7.7%
I35920
 
6.3%
P32692
 
5.7%
O26516
 
4.6%
L22719
 
4.0%
Other values (16)107066
18.7%
Common
ValueCountFrequency (%)
15621
78.5%
.1564
 
7.9%
-989
 
5.0%
(338
 
1.7%
)336
 
1.7%
,321
 
1.6%
/283
 
1.4%
&271
 
1.4%
'35
 
0.2%
?22
 
0.1%
Other values (11)117
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII593589
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E82988
14.0%
S66877
11.3%
A56791
9.6%
N50502
 
8.5%
C47336
 
8.0%
R44285
 
7.5%
I35920
 
6.1%
P32692
 
5.5%
O26516
 
4.5%
L22719
 
3.8%
Other values (37)126963
21.4%

model
Categorical

HIGH CARDINALITY

Distinct11330
Distinct (%)14.3%
Missing118
Missing (%)0.1%
Memory size619.6 KiB
152
 
2278
172
 
1263
172N
 
1133
PA-28-140
 
900
172M
 
775
Other values (11325)
72826 

Length

Max length20
Median length5
Mean length5.828215977
Min length1

Characters and Unicode

Total characters461449
Distinct characters82
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7068 ?
Unique (%)8.9%

Sample

1st row64
2nd rowKR-2
3rd rowWINDWAGON
4th rowMIDGET MUSTANG
5th rowSKYBOLT

Common Values

ValueCountFrequency (%)
1522278
 
2.9%
1721263
 
1.6%
172N1133
 
1.4%
PA-28-140900
 
1.1%
172M775
 
1.0%
150725
 
0.9%
172P669
 
0.8%
150M581
 
0.7%
PA-18573
 
0.7%
PA-28-161558
 
0.7%
Other values (11320)69720
87.9%

Length

2021-09-06T13:05:55.986924image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1522303
 
2.5%
1721329
 
1.5%
172n1135
 
1.2%
ii933
 
1.0%
pa-28-140901
 
1.0%
172m775
 
0.9%
150758
 
0.8%
172p672
 
0.7%
150m581
 
0.6%
pa-18577
 
0.6%
Other values (8845)80852
89.0%

Most occurring characters

ValueCountFrequency (%)
145859
 
9.9%
245323
 
9.8%
-43474
 
9.4%
033868
 
7.3%
A31738
 
6.9%
519580
 
4.2%
818527
 
4.0%
318109
 
3.9%
P17213
 
3.7%
717205
 
3.7%
Other values (72)170553
37.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number223981
48.5%
Uppercase Letter166764
36.1%
Dash Punctuation43474
 
9.4%
Lowercase Letter14723
 
3.2%
Space Separator11641
 
2.5%
Other Punctuation457
 
0.1%
Open Punctuation181
 
< 0.1%
Close Punctuation177
 
< 0.1%
Math Symbol50
 
< 0.1%
Control1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A31738
19.0%
P17213
 
10.3%
R11139
 
6.7%
C10766
 
6.5%
B10571
 
6.3%
T8969
 
5.4%
S8853
 
5.3%
E7887
 
4.7%
I6869
 
4.1%
M6425
 
3.9%
Other values (16)46334
27.8%
Lowercase Letter
ValueCountFrequency (%)
a1806
12.3%
e1649
11.2%
r1497
10.2%
i1305
 
8.9%
t1184
 
8.0%
o1061
 
7.2%
n998
 
6.8%
l709
 
4.8%
s662
 
4.5%
c489
 
3.3%
Other values (16)3363
22.8%
Other Punctuation
ValueCountFrequency (%)
/252
55.1%
.150
32.8%
"18
 
3.9%
'17
 
3.7%
#6
 
1.3%
,6
 
1.3%
&4
 
0.9%
;1
 
0.2%
\1
 
0.2%
:1
 
0.2%
Decimal Number
ValueCountFrequency (%)
145859
20.5%
245323
20.2%
033868
15.1%
519580
8.7%
818527
8.3%
318109
 
8.1%
717205
 
7.7%
410970
 
4.9%
610949
 
4.9%
93591
 
1.6%
Open Punctuation
ValueCountFrequency (%)
(180
99.4%
[1
 
0.6%
Close Punctuation
ValueCountFrequency (%)
)176
99.4%
]1
 
0.6%
Math Symbol
ValueCountFrequency (%)
+48
96.0%
=2
 
4.0%
Dash Punctuation
ValueCountFrequency (%)
-43474
100.0%
Space Separator
ValueCountFrequency (%)
11641
100.0%
Control
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common279962
60.7%
Latin181487
39.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
A31738
17.5%
P17213
 
9.5%
R11139
 
6.1%
C10766
 
5.9%
B10571
 
5.8%
T8969
 
4.9%
S8853
 
4.9%
E7887
 
4.3%
I6869
 
3.8%
M6425
 
3.5%
Other values (42)61057
33.6%
Common
ValueCountFrequency (%)
145859
16.4%
245323
16.2%
-43474
15.5%
033868
12.1%
519580
7.0%
818527
6.6%
318109
 
6.5%
717205
 
6.1%
11641
 
4.2%
410970
 
3.9%
Other values (20)15406
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII461449
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
145859
 
9.9%
245323
 
9.8%
-43474
 
9.4%
033868
 
7.3%
A31738
 
6.9%
519580
 
4.2%
818527
 
4.0%
318109
 
3.9%
P17213
 
3.7%
717205
 
3.7%
Other values (72)170553
37.0%

amateur_build
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing572
Missing (%)0.7%
Memory size155.0 KiB
False
71105 
True
7616 
(Missing)
 
572
ValueCountFrequency (%)
False71105
89.7%
True7616
 
9.6%
(Missing)572
 
0.7%
2021-09-06T13:05:56.036421image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

number_of_engines
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing3986
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean1.146042201
Minimum0
Maximum18
Zeros1275
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size619.6 KiB
2021-09-06T13:05:56.069969image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum18
Range18
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4559854315
Coefficient of variation (CV)0.3978783951
Kurtosis34.30191777
Mean1.146042201
Median Absolute Deviation (MAD)0
Skewness3.02838621
Sum86305
Variance0.2079227137
MonotonicityNot monotonic
2021-09-06T13:05:56.120362image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
163082
79.6%
210057
 
12.7%
01275
 
1.6%
3477
 
0.6%
4415
 
0.5%
181
 
< 0.1%
(Missing)3986
 
5.0%
ValueCountFrequency (%)
01275
 
1.6%
163082
79.6%
210057
 
12.7%
3477
 
0.6%
4415
 
0.5%
181
 
< 0.1%
ValueCountFrequency (%)
181
 
< 0.1%
4415
 
0.5%
3477
 
0.6%
210057
 
12.7%
163082
79.6%
01275
 
1.6%

engine_type
Categorical

HIGH CORRELATION
MISSING

Distinct14
Distinct (%)< 0.1%
Missing3374
Missing (%)4.3%
Memory size619.6 KiB
Reciprocating
64598 
Turbo Shaft
 
3305
Turbo Prop
 
3042
Turbo Fan
 
2226
Unknown
 
2052
Other values (9)
 
696

Length

Max length16
Median length13
Mean length12.47633662
Min length4

Characters and Unicode

Total characters947191
Distinct characters33
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowReciprocating
2nd rowReciprocating
3rd rowReciprocating
4th rowReciprocating
5th rowReciprocating

Common Values

ValueCountFrequency (%)
Reciprocating64598
81.5%
Turbo Shaft3305
 
4.2%
Turbo Prop3042
 
3.8%
Turbo Fan2226
 
2.8%
Unknown2052
 
2.6%
Turbo Jet678
 
0.9%
None6
 
< 0.1%
Electric3
 
< 0.1%
TF, TJ3
 
< 0.1%
REC, TJ, TJ2
 
< 0.1%
Other values (4)4
 
< 0.1%
(Missing)3374
 
4.3%

Length

2021-09-06T13:05:56.251472image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
reciprocating64598
75.8%
turbo9251
 
10.9%
shaft3305
 
3.9%
prop3042
 
3.6%
fan2226
 
2.6%
unknown2052
 
2.4%
jet678
 
0.8%
tj11
 
< 0.1%
rec7
 
< 0.1%
none6
 
< 0.1%
Other values (5)9
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
c129203
13.6%
i129200
13.6%
o78950
8.3%
r76895
8.1%
n72986
7.7%
a70129
7.4%
t68585
7.2%
p67640
7.1%
e65286
6.9%
R64606
6.8%
Other values (23)123711
13.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter852695
90.0%
Uppercase Letter85216
 
9.0%
Space Separator9266
 
1.0%
Other Punctuation14
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c129203
15.2%
i129200
15.2%
o78950
9.3%
r76895
9.0%
n72986
8.6%
a70129
8.2%
t68585
8.0%
p67640
7.9%
e65286
7.7%
g64598
7.6%
Other values (9)29223
 
3.4%
Uppercase Letter
ValueCountFrequency (%)
R64606
75.8%
T9265
 
10.9%
S3305
 
3.9%
P3042
 
3.6%
F2229
 
2.6%
U2052
 
2.4%
J689
 
0.8%
E12
 
< 0.1%
C8
 
< 0.1%
N6
 
< 0.1%
Other values (2)2
 
< 0.1%
Space Separator
ValueCountFrequency (%)
9266
100.0%
Other Punctuation
ValueCountFrequency (%)
,14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin937911
99.0%
Common9280
 
1.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
c129203
13.8%
i129200
13.8%
o78950
8.4%
r76895
8.2%
n72986
7.8%
a70129
7.5%
t68585
7.3%
p67640
7.2%
e65286
7.0%
R64606
6.9%
Other values (21)114431
12.2%
Common
ValueCountFrequency (%)
9266
99.8%
,14
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII947191
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
c129203
13.6%
i129200
13.6%
o78950
8.3%
r76895
8.1%
n72986
7.7%
a70129
7.4%
t68585
7.2%
p67640
7.1%
e65286
6.9%
R64606
6.8%
Other values (23)123711
13.1%

total_fatal_injuries
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED
ZEROS

Distinct122
Distinct (%)0.2%
Missing23309
Missing (%)29.4%
Infinite0
Infinite (%)0.0%
Mean0.8146791941
Minimum0
Maximum349
Zeros40092
Zeros (%)50.6%
Negative0
Negative (%)0.0%
Memory size619.6 KiB
2021-09-06T13:05:56.319357image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum349
Range349
Interquartile range (IQR)1

Descriptive statistics

Standard deviation6.23370003
Coefficient of variation (CV)7.651723618
Kurtosis1082.130068
Mean0.8146791941
Median Absolute Deviation (MAD)0
Skewness29.51903889
Sum45609
Variance38.85901607
MonotonicityNot monotonic
2021-09-06T13:05:56.391865image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
040092
50.6%
17847
 
9.9%
24619
 
5.8%
31451
 
1.8%
41012
 
1.3%
5311
 
0.4%
6196
 
0.2%
783
 
0.1%
865
 
0.1%
1042
 
0.1%
Other values (112)266
 
0.3%
(Missing)23309
29.4%
ValueCountFrequency (%)
040092
50.6%
17847
 
9.9%
24619
 
5.8%
31451
 
1.8%
41012
 
1.3%
5311
 
0.4%
6196
 
0.2%
783
 
0.1%
865
 
0.1%
936
 
< 0.1%
ValueCountFrequency (%)
3492
< 0.1%
2951
< 0.1%
2701
< 0.1%
2651
< 0.1%
2561
< 0.1%
2391
< 0.1%
2301
< 0.1%
2291
< 0.1%
2282
< 0.1%
2241
< 0.1%

total_serious_injuries
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED
ZEROS

Distinct40
Distinct (%)0.1%
Missing25551
Missing (%)32.2%
Infinite0
Infinite (%)0.0%
Mean0.3177031
Minimum0
Maximum111
Zeros42660
Zeros (%)53.8%
Negative0
Negative (%)0.0%
Memory size619.6 KiB
2021-09-06T13:05:56.470892image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum111
Range111
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.372924237
Coefficient of variation (CV)4.321406485
Kurtosis2213.530689
Mean0.3177031
Median Absolute Deviation (MAD)0
Skewness37.45361961
Sum17074
Variance1.884920959
MonotonicityNot monotonic
2021-09-06T13:05:56.536080image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
042660
53.8%
18016
 
10.1%
22177
 
2.7%
3502
 
0.6%
4205
 
0.3%
565
 
0.1%
625
 
< 0.1%
722
 
< 0.1%
87
 
< 0.1%
107
 
< 0.1%
Other values (30)56
 
0.1%
(Missing)25551
32.2%
ValueCountFrequency (%)
042660
53.8%
18016
 
10.1%
22177
 
2.7%
3502
 
0.6%
4205
 
0.3%
565
 
0.1%
625
 
< 0.1%
722
 
< 0.1%
87
 
< 0.1%
97
 
< 0.1%
ValueCountFrequency (%)
1111
 
< 0.1%
1061
 
< 0.1%
811
 
< 0.1%
661
 
< 0.1%
601
 
< 0.1%
592
< 0.1%
551
 
< 0.1%
503
< 0.1%
471
 
< 0.1%
451
 
< 0.1%

total_minor_injuries
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED
ZEROS

Distinct62
Distinct (%)0.1%
Missing24460
Missing (%)30.8%
Infinite0
Infinite (%)0.0%
Mean0.5025805628
Minimum0
Maximum380
Zeros40064
Zeros (%)50.5%
Negative0
Negative (%)0.0%
Memory size619.6 KiB
2021-09-06T13:05:56.607344image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum380
Range380
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.781994444
Coefficient of variation (CV)5.53541989
Kurtosis7360.610457
Mean0.5025805628
Median Absolute Deviation (MAD)0
Skewness66.84890696
Sum27558
Variance7.739493085
MonotonicityNot monotonic
2021-09-06T13:05:56.678994image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
040064
50.5%
19519
 
12.0%
23575
 
4.5%
3799
 
1.0%
4389
 
0.5%
5136
 
0.2%
672
 
0.1%
758
 
0.1%
928
 
< 0.1%
823
 
< 0.1%
Other values (52)170
 
0.2%
(Missing)24460
30.8%
ValueCountFrequency (%)
040064
50.5%
19519
 
12.0%
23575
 
4.5%
3799
 
1.0%
4389
 
0.5%
5136
 
0.2%
672
 
0.1%
758
 
0.1%
823
 
< 0.1%
928
 
< 0.1%
ValueCountFrequency (%)
3801
< 0.1%
2001
< 0.1%
1711
< 0.1%
1371
< 0.1%
1251
< 0.1%
961
< 0.1%
881
< 0.1%
841
< 0.1%
711
< 0.1%
691
< 0.1%

total_uninjured
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct364
Distinct (%)0.5%
Missing12344
Missing (%)15.6%
Infinite0
Infinite (%)0.0%
Mean5.790885599
Minimum0
Maximum699
Zeros19126
Zeros (%)24.1%
Negative0
Negative (%)0.0%
Memory size619.6 KiB
2021-09-06T13:05:56.754487image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile6
Maximum699
Range699
Interquartile range (IQR)2

Descriptive statistics

Standard deviation29.22301628
Coefficient of variation (CV)5.046381211
Kurtosis101.5231507
Mean5.790885599
Median Absolute Deviation (MAD)1
Skewness8.950895422
Sum387694
Variance853.9846806
MonotonicityNot monotonic
2021-09-06T13:05:56.828505image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
122583
28.5%
019126
24.1%
214316
18.1%
33949
 
5.0%
42508
 
3.2%
5817
 
1.0%
6449
 
0.6%
7253
 
0.3%
8141
 
0.2%
9111
 
0.1%
Other values (354)2696
 
3.4%
(Missing)12344
15.6%
ValueCountFrequency (%)
019126
24.1%
122583
28.5%
214316
18.1%
33949
 
5.0%
42508
 
3.2%
5817
 
1.0%
6449
 
0.6%
7253
 
0.3%
8141
 
0.2%
9111
 
0.1%
ValueCountFrequency (%)
6992
< 0.1%
5882
< 0.1%
5762
< 0.1%
5732
< 0.1%
5581
< 0.1%
5282
< 0.1%
5071
< 0.1%
5012
< 0.1%
4952
< 0.1%
4612
< 0.1%

weather_conditions
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size619.6 KiB
VMC
70507 
IMC
 
5660
UNK
 
3126

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters237879
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVMC
2nd rowVMC
3rd rowVMC
4th rowVMC
5th rowVMC

Common Values

ValueCountFrequency (%)
VMC70507
88.9%
IMC5660
 
7.1%
UNK3126
 
3.9%

Length

2021-09-06T13:05:56.966692image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-06T13:05:57.006560image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
vmc70507
88.9%
imc5660
 
7.1%
unk3126
 
3.9%

Most occurring characters

ValueCountFrequency (%)
M76167
32.0%
C76167
32.0%
V70507
29.6%
I5660
 
2.4%
U3126
 
1.3%
N3126
 
1.3%
K3126
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter237879
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M76167
32.0%
C76167
32.0%
V70507
29.6%
I5660
 
2.4%
U3126
 
1.3%
N3126
 
1.3%
K3126
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
Latin237879
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M76167
32.0%
C76167
32.0%
V70507
29.6%
I5660
 
2.4%
U3126
 
1.3%
N3126
 
1.3%
K3126
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII237879
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M76167
32.0%
C76167
32.0%
V70507
29.6%
I5660
 
2.4%
U3126
 
1.3%
N3126
 
1.3%
K3126
 
1.3%

phase_of_flight
Categorical

HIGH CORRELATION
MISSING

Distinct12
Distinct (%)< 0.1%
Missing6054
Missing (%)7.6%
Memory size619.6 KiB
LANDING
19209 
TAKEOFF
15284 
CRUISE
10749 
MANEUVERING
9818 
APPROACH
7720 
Other values (7)
10459 

Length

Max length11
Median length7
Mean length7.393779271
Min length4

Characters and Unicode

Total characters541513
Distinct characters23
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCLIMB
2nd rowCRUISE
3rd rowAPPROACH
4th rowMANEUVERING
5th rowLANDING

Common Values

ValueCountFrequency (%)
LANDING19209
24.2%
TAKEOFF15284
19.3%
CRUISE10749
13.6%
MANEUVERING9818
12.4%
APPROACH7720
9.7%
TAXI2322
 
2.9%
CLIMB2279
 
2.9%
DESCENT2202
 
2.8%
GO-AROUND1608
 
2.0%
STANDING1219
 
1.5%
Other values (2)829
 
1.0%
(Missing)6054
 
7.6%

Length

2021-09-06T13:05:57.129438image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
landing19209
26.2%
takeoff15284
20.9%
cruise10749
14.7%
maneuvering9818
13.4%
approach7720
10.5%
taxi2322
 
3.2%
climb2279
 
3.1%
descent2202
 
3.0%
go-around1608
 
2.2%
standing1219
 
1.7%
Other values (2)829
 
1.1%

Most occurring characters

ValueCountFrequency (%)
N66318
12.2%
A64900
12.0%
E50230
 
9.3%
I45596
 
8.4%
G31854
 
5.9%
F30568
 
5.6%
R30052
 
5.5%
O27049
 
5.0%
D24238
 
4.5%
C22950
 
4.2%
Other values (13)147758
27.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter539905
99.7%
Dash Punctuation1608
 
0.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N66318
12.3%
A64900
12.0%
E50230
 
9.3%
I45596
 
8.4%
G31854
 
5.9%
F30568
 
5.7%
R30052
 
5.6%
O27049
 
5.0%
D24238
 
4.5%
C22950
 
4.3%
Other values (12)146150
27.1%
Dash Punctuation
ValueCountFrequency (%)
-1608
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin539905
99.7%
Common1608
 
0.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
N66318
12.3%
A64900
12.0%
E50230
 
9.3%
I45596
 
8.4%
G31854
 
5.9%
F30568
 
5.7%
R30052
 
5.6%
O27049
 
5.0%
D24238
 
4.5%
C22950
 
4.3%
Other values (12)146150
27.1%
Common
ValueCountFrequency (%)
-1608
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII541513
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N66318
12.2%
A64900
12.0%
E50230
 
9.3%
I45596
 
8.4%
G31854
 
5.9%
F30568
 
5.6%
R30052
 
5.5%
O27049
 
5.0%
D24238
 
4.5%
C22950
 
4.2%
Other values (13)147758
27.3%

Year
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct42
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1996.749827
Minimum1948
Maximum2017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size619.6 KiB
2021-09-06T13:05:57.196967image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1948
5-th percentile1983
Q11988
median1996
Q32005
95-th percentile2014
Maximum2017
Range69
Interquartile range (IQR)17

Descriptive statistics

Standard deviation10.11297874
Coefficient of variation (CV)0.005064719981
Kurtosis-1.155373615
Mean1996.749827
Median Absolute Deviation (MAD)9
Skewness0.2332184642
Sum158328284
Variance102.2723391
MonotonicityNot monotonic
2021-09-06T13:05:57.267598image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
19823593
 
4.5%
19833556
 
4.5%
19843457
 
4.4%
19853096
 
3.9%
19862880
 
3.6%
19872828
 
3.6%
19882730
 
3.4%
19892544
 
3.2%
19902518
 
3.2%
19912462
 
3.1%
Other values (32)49629
62.6%
ValueCountFrequency (%)
19481
 
< 0.1%
19621
 
< 0.1%
19741
 
< 0.1%
19771
 
< 0.1%
19791
 
< 0.1%
19811
 
< 0.1%
19823593
4.5%
19833556
4.5%
19843457
4.4%
19853096
3.9%
ValueCountFrequency (%)
20171
 
< 0.1%
20161409
1.8%
20151578
2.0%
20141539
1.9%
20131555
2.0%
20121860
2.3%
20111886
2.4%
20101818
2.3%
20091805
2.3%
20081931
2.4%

Month
Real number (ℝ≥0)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.582787888
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size619.6 KiB
2021-09-06T13:05:57.334143image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.060089058
Coefficient of variation (CV)0.4648621694
Kurtosis-0.9007937113
Mean6.582787888
Median Absolute Deviation (MAD)2
Skewness-0.05644265097
Sum521969
Variance9.364145045
MonotonicityNot monotonic
2021-09-06T13:05:57.384660image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
79504
12.0%
88983
11.3%
68544
10.8%
57626
9.6%
97382
9.3%
46549
8.3%
106165
7.8%
35994
7.6%
114907
6.2%
24680
5.9%
Other values (2)8959
11.3%
ValueCountFrequency (%)
14448
5.6%
24680
5.9%
35994
7.6%
46549
8.3%
57626
9.6%
68544
10.8%
79504
12.0%
88983
11.3%
97382
9.3%
106165
7.8%
ValueCountFrequency (%)
124511
5.7%
114907
6.2%
106165
7.8%
97382
9.3%
88983
11.3%
79504
12.0%
68544
10.8%
57626
9.6%
46549
8.3%
35994
7.6%

Day
Real number (ℝ≥0)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.71744038
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size619.6 KiB
2021-09-06T13:05:57.442129image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile30
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.831329784
Coefficient of variation (CV)0.5618809152
Kurtosis-1.195828365
Mean15.71744038
Median Absolute Deviation (MAD)8
Skewness0.006435342317
Sum1246283
Variance77.99238576
MonotonicityNot monotonic
2021-09-06T13:05:57.503742image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
12723
 
3.4%
192685
 
3.4%
22675
 
3.4%
162667
 
3.4%
62661
 
3.4%
182646
 
3.3%
172639
 
3.3%
52638
 
3.3%
232635
 
3.3%
82633
 
3.3%
Other values (21)52691
66.5%
ValueCountFrequency (%)
12723
3.4%
22675
3.4%
32541
3.2%
42599
3.3%
52638
3.3%
62661
3.4%
72600
3.3%
82633
3.3%
92554
3.2%
102597
3.3%
ValueCountFrequency (%)
311616
2.0%
302392
3.0%
292409
3.0%
282628
3.3%
272623
3.3%
262606
3.3%
252541
3.2%
242520
3.2%
232635
3.3%
222520
3.2%

injuries
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED
ZEROS

Distinct97
Distinct (%)0.2%
Missing29557
Missing (%)37.3%
Infinite0
Infinite (%)0.0%
Mean1.071155702
Minimum0
Maximum283
Zeros26956
Zeros (%)34.0%
Negative0
Negative (%)0.0%
Memory size619.6 KiB
2021-09-06T13:05:57.574092image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum283
Range283
Interquartile range (IQR)1

Descriptive statistics

Standard deviation5.048778214
Coefficient of variation (CV)4.713393398
Kurtosis1208.600025
Mean1.071155702
Median Absolute Deviation (MAD)0
Skewness30.77744311
Sum53275
Variance25.49016146
MonotonicityNot monotonic
2021-09-06T13:05:57.641908image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
026956
34.0%
111453
 
14.4%
26910
 
8.7%
31880
 
2.4%
41384
 
1.7%
5395
 
0.5%
6257
 
0.3%
7101
 
0.1%
879
 
0.1%
1039
 
< 0.1%
Other values (87)282
 
0.4%
(Missing)29557
37.3%
ValueCountFrequency (%)
026956
34.0%
111453
14.4%
26910
 
8.7%
31880
 
2.4%
41384
 
1.7%
5395
 
0.5%
6257
 
0.3%
7101
 
0.1%
879
 
0.1%
936
 
< 0.1%
ValueCountFrequency (%)
2831
< 0.1%
2751
< 0.1%
2561
< 0.1%
2311
< 0.1%
2301
< 0.1%
2291
< 0.1%
1902
< 0.1%
1891
< 0.1%
1741
< 0.1%
1711
< 0.1%

pax_onboard
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct322
Distinct (%)0.6%
Missing29671
Missing (%)37.4%
Infinite0
Infinite (%)0.0%
Mean5.286304462
Minimum0
Maximum528
Zeros27
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size619.6 KiB
2021-09-06T13:05:57.713643image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q32
95-th percentile6
Maximum528
Range528
Interquartile range (IQR)1

Descriptive statistics

Standard deviation24.70287163
Coefficient of variation (CV)4.672994492
Kurtosis128.5555772
Mean5.286304462
Median Absolute Deviation (MAD)1
Skewness10.25386491
Sum262317
Variance610.231867
MonotonicityNot monotonic
2021-09-06T13:05:57.789059image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
121780
27.5%
215873
20.0%
34687
 
5.9%
43393
 
4.3%
51043
 
1.3%
6597
 
0.8%
7263
 
0.3%
8168
 
0.2%
991
 
0.1%
1089
 
0.1%
Other values (312)1638
 
2.1%
(Missing)29671
37.4%
ValueCountFrequency (%)
027
 
< 0.1%
121780
27.5%
215873
20.0%
34687
 
5.9%
43393
 
4.3%
51043
 
1.3%
6597
 
0.8%
7263
 
0.3%
8168
 
0.2%
991
 
0.1%
ValueCountFrequency (%)
5282
< 0.1%
5071
< 0.1%
4962
< 0.1%
4811
< 0.1%
4681
< 0.1%
4612
< 0.1%
4592
< 0.1%
4412
< 0.1%
4402
< 0.1%
4362
< 0.1%

fatality_percentage
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct114
Distinct (%)0.2%
Missing29698
Missing (%)37.5%
Infinite0
Infinite (%)0.0%
Mean17.37817274
Minimum0
Maximum100
Zeros40061
Zeros (%)50.5%
Negative0
Negative (%)0.0%
Memory size619.6 KiB
2021-09-06T13:05:57.863314image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)0

Descriptive statistics

Standard deviation36.86760591
Coefficient of variation (CV)2.121489207
Kurtosis1.055932869
Mean17.37817274
Median Absolute Deviation (MAD)0
Skewness1.722139158
Sum861870.477
Variance1359.220365
MonotonicityNot monotonic
2021-09-06T13:05:57.940143image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
040061
50.5%
1007802
 
9.8%
50861
 
1.1%
33.33333333209
 
0.3%
66.66666667157
 
0.2%
25109
 
0.1%
7587
 
0.1%
2044
 
0.1%
4030
 
< 0.1%
6025
 
< 0.1%
Other values (104)210
 
0.3%
(Missing)29698
37.5%
ValueCountFrequency (%)
040061
50.5%
0.25445292621
 
< 0.1%
0.29850746271
 
< 0.1%
0.35842293911
 
< 0.1%
0.40816326531
 
< 0.1%
0.49261083741
 
< 0.1%
0.62893081762
 
< 0.1%
0.66666666671
 
< 0.1%
0.74074074071
 
< 0.1%
0.75187969921
 
< 0.1%
ValueCountFrequency (%)
1007802
9.8%
98.59154931
 
< 0.1%
98.181818181
 
< 0.1%
98.113207551
 
< 0.1%
97.560975611
 
< 0.1%
96.29629631
 
< 0.1%
94.957983191
 
< 0.1%
93.506493511
 
< 0.1%
91.111111112
 
< 0.1%
90.6251
 
< 0.1%

survived
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct319
Distinct (%)0.6%
Missing29671
Missing (%)37.4%
Infinite0
Infinite (%)0.0%
Mean4.813913184
Minimum0
Maximum528
Zeros7829
Zeros (%)9.9%
Negative0
Negative (%)0.0%
Memory size619.6 KiB
2021-09-06T13:05:58.013982image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile5
Maximum528
Range528
Interquartile range (IQR)1

Descriptive statistics

Standard deviation24.4240241
Coefficient of variation (CV)5.073632026
Kurtosis133.225737
Mean4.813913184
Median Absolute Deviation (MAD)1
Skewness10.44181691
Sum238876
Variance596.5329533
MonotonicityNot monotonic
2021-09-06T13:05:58.088950image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
119183
24.2%
213004
16.4%
07829
 
9.9%
33771
 
4.8%
42578
 
3.3%
5812
 
1.0%
6447
 
0.6%
7224
 
0.3%
8117
 
0.1%
971
 
0.1%
Other values (309)1586
 
2.0%
(Missing)29671
37.4%
ValueCountFrequency (%)
07829
9.9%
119183
24.2%
213004
16.4%
33771
 
4.8%
42578
 
3.3%
5812
 
1.0%
6447
 
0.6%
7224
 
0.3%
8117
 
0.1%
971
 
0.1%
ValueCountFrequency (%)
5282
< 0.1%
5071
< 0.1%
4962
< 0.1%
4811
< 0.1%
4681
< 0.1%
4612
< 0.1%
4592
< 0.1%
4412
< 0.1%
4402
< 0.1%
4362
< 0.1%

AmateurBuilt
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.6 KiB
False
71647 
True
7646 
ValueCountFrequency (%)
False71647
90.4%
True7646
 
9.6%
2021-09-06T13:05:58.136340image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

df_index
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct79293
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39646
Minimum0
Maximum79292
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size619.6 KiB
2021-09-06T13:05:58.185245image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3964.6
Q119823
median39646
Q359469
95-th percentile75327.4
Maximum79292
Range79292
Interquartile range (IQR)39646

Descriptive statistics

Standard deviation22890.06178
Coefficient of variation (CV)0.5773611912
Kurtosis-1.2
Mean39646
Median Absolute Deviation (MAD)19823
Skewness-2.103152104 × 10-17
Sum3143650278
Variance523954928.5
MonotonicityNot monotonic
2021-09-06T13:05:58.263548image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20471
 
< 0.1%
47591
 
< 0.1%
108961
 
< 0.1%
88491
 
< 0.1%
149941
 
< 0.1%
129471
 
< 0.1%
27081
 
< 0.1%
6611
 
< 0.1%
68061
 
< 0.1%
272881
 
< 0.1%
Other values (79283)79283
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
792921
< 0.1%
792911
< 0.1%
792901
< 0.1%
792891
< 0.1%
792881
< 0.1%
792871
< 0.1%
792861
< 0.1%
792851
< 0.1%
792841
< 0.1%
792831
< 0.1%

Interactions

2021-09-06T13:05:37.947547image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:38.042841image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:38.124105image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:38.203427image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:38.280105image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:38.375841image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:38.458484image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:38.540505image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:38.623688image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:38.699771image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:38.774453image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:38.849669image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:38.953653image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:39.180830image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:39.286688image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:39.379561image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:39.456603image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:39.532003image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:39.611958image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:39.689376image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:39.773276image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:39.854875image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:39.937745image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:40.011215image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:40.085561image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:40.164227image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:40.378755image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:40.462132image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:40.544188image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:40.632023image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:40.705733image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:40.780919image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:40.848818image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:40.920629image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:40.989634image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:41.065067image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:41.137895image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:41.230615image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:41.297591image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:41.365413image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:41.432985image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:41.501769image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:41.584115image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:41.657373image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:41.736407image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:41.804409image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:41.871879image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:41.949689image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:42.019310image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:42.091689image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:42.162906image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:42.231543image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:42.298984image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:42.366594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:42.434452image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:42.506604image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:42.587992image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:42.667138image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:42.742916image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:42.814816image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:42.888944image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:42.967106image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:43.126202image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:43.201728image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:43.275019image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:43.347316image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:43.421240image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:43.492870image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:43.564922image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:43.641767image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:43.723443image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:43.804734image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:43.882445image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:43.950012image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:44.017187image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:44.091124image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:44.169799image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:44.247290image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:44.321707image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:44.394120image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:44.460868image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:44.531386image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:44.598968image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:44.668965image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:44.749598image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:44.830488image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:44.905365image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:44.980243image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:45.053742image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:45.129845image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:45.208007image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:45.287224image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:45.363134image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:45.436198image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:45.505981image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:45.576229image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:45.646766image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:45.720382image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:45.801474image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:45.876620image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:45.947853image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:46.019107image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:46.086478image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:46.158749image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:46.232704image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:46.402875image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:46.473751image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:46.541695image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:46.608200image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:46.674937image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:46.741949image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:46.811917image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:46.888407image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:46.995208image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:47.068916image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:47.141533image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:47.215073image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:47.289943image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:47.370882image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:47.460699image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:47.535415image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:47.604800image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:47.673108image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:47.741586image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:47.810912image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:47.883882image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:47.958901image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:48.030238image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:48.101434image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:48.168140image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:48.238620image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:48.308640image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:48.376399image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:48.446201image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:48.513347image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:48.580163image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:48.645840image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:48.712393image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:48.779168image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:48.846383image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:48.923027image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:48.995288image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:49.067079image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:49.134588image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:49.202020image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:49.272749image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:49.340523image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:49.410127image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:49.477228image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:49.545403image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:49.612339image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:49.678917image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:49.804360image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:49.874357image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:49.951290image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:50.024233image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:50.142659image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:50.240203image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:50.310956image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:50.599258image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:50.669201image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:50.739669image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:50.810994image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:50.879288image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:50.946121image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:51.013070image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:51.080089image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:51.148390image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:51.222741image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:51.295208image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:51.366627image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:51.433211image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:51.500511image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:51.570269image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:51.637818image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:51.707185image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:51.773777image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:51.840833image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:51.907051image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:51.973533image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:52.040265image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:52.107361image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:52.184501image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:52.272051image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:52.351089image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:52.429637image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:52.504843image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:52.584966image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:52.666218image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:52.753776image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:52.831280image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:52.910676image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:52.988149image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:53.062859image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:53.137901image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-06T13:05:53.215721image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-09-06T13:05:58.343078image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-09-06T13:05:58.461973image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-09-06T13:05:58.581456image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-09-06T13:05:58.707660image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-09-06T13:05:58.836879image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-09-06T13:05:53.424750image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-09-06T13:05:53.887999image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-09-06T13:05:54.357578image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-09-06T13:05:54.621447image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

Unnamed: 0event_datelocationinjury_severityaircraft_damagemakemodelamateur_buildnumber_of_enginesengine_typetotal_fatal_injuriestotal_serious_injuriestotal_minor_injuriestotal_uninjuredweather_conditionsphase_of_flightYearMonthDayinjuriespax_onboardfatality_percentagesurvivedAmateurBuiltdf_index
0777381982-06-13CAMBRIA, NYNon-FatalDestroyedNaN64No1.0Reciprocating0.01.00.00.0VMCCLIMB19826131.01.00.01.0No77738
1775261982-07-01MCWHORTER, KYNon-FatalDestroyedNaNKR-2Yes1.0Reciprocating0.01.01.00.0VMCCRUISE1982712.02.00.02.0Yes77526
2773251982-07-16FREDERICK, MDFatal(1)DestroyedNaNWINDWAGONYes1.0Reciprocating1.00.00.00.0VMCAPPROACH19827161.01.0100.00.0Yes77325
3768251982-08-21VENTURA, CANon-FatalDestroyedNaNMIDGET MUSTANGYes1.0Reciprocating0.00.00.01.0VMCMANEUVERING19828210.01.00.01.0Yes76825
4767861982-08-24SIDNEY, NENon-FatalSubstantialNaNSKYBOLTYes1.0Reciprocating0.00.00.01.0VMCLANDING19828240.01.00.01.0Yes76786
5765591982-09-11PLATTSBURG, MONon-FatalSubstantialNaNSTARDUSTER TOOYes1.0Reciprocating0.02.00.00.0VMCTAKEOFF19829112.02.00.02.0Yes76559
6761821982-10-23ELOY, AZFatal(1)SubstantialNaNHOBBS B8MYes1.0Reciprocating1.00.00.00.0VMCAPPROACH198210231.01.0100.00.0Yes76182
7523591990-11-02IOWA PARK, TXNon-FatalSubstantialNaNRANS S-9Yes1.0Reciprocating0.00.00.01.0VMCTAKEOFF19901120.01.00.01.0Yes52359
8339351998-12-05MANILLA, PhilippinesUnavailableNaNNaNA330NoNaNUnknownNaNNaNNaNNaNUNKNaN1998125NaNNaNNaNNaNNo33935
9295392000-11-28Nairobi, KenyaIncidentMinorNaNNaNNoNaNNaNNaNNaNNaNNaNUNKNaN20001128NaNNaNNaNNaNNo29539

Last rows

Unnamed: 0event_datelocationinjury_severityaircraft_damagemakemodelamateur_buildnumber_of_enginesengine_typetotal_fatal_injuriestotal_serious_injuriestotal_minor_injuriestotal_uninjuredweather_conditionsphase_of_flightYearMonthDayinjuriespax_onboardfatality_percentagesurvivedAmateurBuiltdf_index
79283104692010-08-17Toledo, SpainFatal(1)DestroyedZIVKO AERONAUTICS INCEDGE 540No1.0Reciprocating1.0NaNNaNNaNUNKNaN2010817NaNNaNNaNNaNNo10469
792847852016-06-16Kyvi­kes, LithuaniaFatal(1)DestroyedZIVKO AERONAUTICS INCEDGE 540NoNaNReciprocating1.0NaNNaNNaNUNKNaN2016616NaNNaNNaNNaNNo785
79285200952005-07-30Denison, TXFatal(1)DestroyedZIVKO AERONAUTICS INC.Edge 540-TYes1.0Reciprocating1.0NaNNaNNaNVMCMANEUVERING2005730NaNNaNNaNNaNYes20095
7928612432016-02-27Hong Kong, Hong KongFatal(1)SubstantialZLINZ242LNoNaNReciprocating1.0NaNNaNNaNUNKUNKNOWN2016227NaNNaNNaNNaNNo1243
79287328771999-06-22GROSSENHAIN, GermanyFatal(4)SubstantialZLINZ-42MNoNaNNaN4.0NaNNaNNaNUNKNaN1999622NaNNaNNaNNaNNo32877
79288161182007-08-15Mosquero, NMFatal(2)SubstantialZLIN AVIATION S.R.O.SavageNo1.0Reciprocating2.0NaNNaNNaNVMCMANEUVERING2007815NaNNaNNaNNaNNo16118
79289225492004-05-29Mazon, ILFatal(1)SubstantialZORNEAA Sport Bi-PlaneYes1.0Reciprocating1.0NaNNaNNaNVMCTAKEOFF2004529NaNNaNNaNNaNYes22549
7929037672014-07-06Mattituck, NYFatal(1)SubstantialZUBAIR S KHANRAVENYes1.0Reciprocating1.0NaNNaNNaNVMCMANEUVERING201476NaNNaNNaNNaNYes3767
79291383611996-11-23WILLIAMSPORT, PANon-FatalSubstantialZUKOWSKIEAA BIPLANEYes1.0Reciprocating0.00.00.01.0VMCTAKEOFF199611230.01.00.01.0Yes38361
79292358631998-02-22WEYERS CAVE, VANon-FatalSubstantialZWARTKIT FOX VIXENYes1.0Reciprocating0.00.00.02.0VMCDESCENT19982220.02.00.02.0Yes35863